{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PanDerm - Skin Lesion Segmentation Evaluation\n",
    "\n",
    "This is a jupyter notebook evaluation of skin lesion segmentation using PanDerm on Ham10000 dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/wangzh/anaconda3/envs/skinfm/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/data/wangzh/anaconda3/envs/skinfm/lib/python3.9/site-packages/flash/__init__.py:21: FutureWarning: In the future `np.object` will be defined as the corresponding NumPy scalar.\n",
      "  if not hasattr(numpy, tp_name):\n",
      "/data/wangzh/anaconda3/envs/skinfm/lib/python3.9/site-packages/flash/__init__.py:21: FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.\n",
      "  if not hasattr(numpy, tp_name):\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import cv2\n",
    "import numpy as np\n",
    "from torchvision import transforms\n",
    "from skimage.segmentation import mark_boundaries\n",
    "from PIL import Image\n",
    "from models.cae_seg import CAEv2_seg\n",
    "from utils.train_utils import largestConnectComponent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to save the generated result. Change the parameter to your own setting\n",
    "save_path = './'\n",
    "os.makedirs(save_path, exist_ok=True)\n",
    "\n",
    "# load dataset\n",
    "image_transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
    "])\n",
    "\n",
    "# Read an image. Change the parameter with your own image path. \n",
    "image = cv2.imread('/data2/wangzh/datasets/Ham10000/images/ISIC_0024338.jpg')\n",
    "image = image[..., ::-1]\n",
    "image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_CUBIC)\n",
    "image = Image.fromarray(np.uint8(image))\n",
    "image = image_transform(image).unsqueeze(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/wangzh/anaconda3/envs/skinfm/lib/python3.9/site-packages/torch/functional.py:507: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1708025847094/work/aten/src/ATen/native/TensorShape.cpp:3549.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "/data/wangzh/anaconda3/envs/skinfm/lib/python3.9/site-packages/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold\n",
      "  warnings.warn('For binary segmentation, we suggest using'\n",
      "/data/wangzh/anaconda3/envs/skinfm/lib/python3.9/site-packages/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` \n",
      "  warnings.warn('``build_loss`` would be deprecated soon, please use '\n",
      "/data/wangzh/anaconda3/envs/skinfm/lib/python3.9/site-packages/mmseg/models/losses/cross_entropy_loss.py:250: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=> Loading CAE weights from /data2/wangzh/checkpoints/caev2_large_checkpoint.pth\n",
      "=> Loading cls_token\n",
      "=> Loading pos_embed\n",
      "=> Loading patch_embed.proj.weight\n",
      "=> Loading patch_embed.proj.bias\n",
      "=> Loading blocks.0.gamma_1\n",
      "=> Loading blocks.0.gamma_2\n",
      "=> Loading blocks.0.norm1.weight\n",
      "=> Loading blocks.0.norm1.bias\n",
      "=> Loading blocks.0.attn.q_bias\n",
      "=> Loading blocks.0.attn.v_bias\n",
      "=> Loading blocks.0.attn.qkv.weight\n",
      "=> Loading blocks.0.attn.proj.weight\n",
      "=> Loading blocks.0.attn.proj.bias\n",
      "=> Loading blocks.0.norm2.weight\n",
      "=> Loading blocks.0.norm2.bias\n",
      "=> Loading blocks.0.mlp.fc1.weight\n",
      "=> Loading blocks.0.mlp.fc1.bias\n",
      "=> Loading blocks.0.mlp.fc2.weight\n",
      "=> Loading blocks.0.mlp.fc2.bias\n",
      "=> Loading blocks.1.gamma_1\n",
      "=> Loading blocks.1.gamma_2\n",
      "=> Loading blocks.1.norm1.weight\n",
      "=> Loading blocks.1.norm1.bias\n",
      "=> Loading blocks.1.attn.q_bias\n",
      "=> Loading blocks.1.attn.v_bias\n",
      "=> Loading blocks.1.attn.qkv.weight\n",
      "=> Loading blocks.1.attn.proj.weight\n",
      "=> Loading blocks.1.attn.proj.bias\n",
      "=> Loading blocks.1.norm2.weight\n",
      "=> Loading blocks.1.norm2.bias\n",
      "=> Loading blocks.1.mlp.fc1.weight\n",
      "=> Loading blocks.1.mlp.fc1.bias\n",
      "=> Loading blocks.1.mlp.fc2.weight\n",
      "=> Loading blocks.1.mlp.fc2.bias\n",
      "=> Loading blocks.2.gamma_1\n",
      "=> Loading blocks.2.gamma_2\n",
      "=> Loading blocks.2.norm1.weight\n",
      "=> Loading blocks.2.norm1.bias\n",
      "=> Loading blocks.2.attn.q_bias\n",
      "=> Loading blocks.2.attn.v_bias\n",
      "=> Loading blocks.2.attn.qkv.weight\n",
      "=> Loading blocks.2.attn.proj.weight\n",
      "=> Loading blocks.2.attn.proj.bias\n",
      "=> Loading blocks.2.norm2.weight\n",
      "=> Loading blocks.2.norm2.bias\n",
      "=> Loading blocks.2.mlp.fc1.weight\n",
      "=> Loading blocks.2.mlp.fc1.bias\n",
      "=> Loading blocks.2.mlp.fc2.weight\n",
      "=> Loading blocks.2.mlp.fc2.bias\n",
      "=> Loading blocks.3.gamma_1\n",
      "=> Loading blocks.3.gamma_2\n",
      "=> Loading blocks.3.norm1.weight\n",
      "=> Loading blocks.3.norm1.bias\n",
      "=> Loading blocks.3.attn.q_bias\n",
      "=> Loading blocks.3.attn.v_bias\n",
      "=> Loading blocks.3.attn.qkv.weight\n",
      "=> Loading blocks.3.attn.proj.weight\n",
      "=> Loading blocks.3.attn.proj.bias\n",
      "=> Loading blocks.3.norm2.weight\n",
      "=> Loading blocks.3.norm2.bias\n",
      "=> Loading blocks.3.mlp.fc1.weight\n",
      "=> Loading blocks.3.mlp.fc1.bias\n",
      "=> Loading blocks.3.mlp.fc2.weight\n",
      "=> Loading blocks.3.mlp.fc2.bias\n",
      "=> Loading blocks.4.gamma_1\n",
      "=> Loading blocks.4.gamma_2\n",
      "=> Loading blocks.4.norm1.weight\n",
      "=> Loading blocks.4.norm1.bias\n",
      "=> Loading blocks.4.attn.q_bias\n",
      "=> Loading blocks.4.attn.v_bias\n",
      "=> Loading blocks.4.attn.qkv.weight\n",
      "=> Loading blocks.4.attn.proj.weight\n",
      "=> Loading blocks.4.attn.proj.bias\n",
      "=> Loading blocks.4.norm2.weight\n",
      "=> Loading blocks.4.norm2.bias\n",
      "=> Loading blocks.4.mlp.fc1.weight\n",
      "=> Loading blocks.4.mlp.fc1.bias\n",
      "=> Loading blocks.4.mlp.fc2.weight\n",
      "=> Loading blocks.4.mlp.fc2.bias\n",
      "=> Loading blocks.5.gamma_1\n",
      "=> Loading blocks.5.gamma_2\n",
      "=> Loading blocks.5.norm1.weight\n",
      "=> Loading blocks.5.norm1.bias\n",
      "=> Loading blocks.5.attn.q_bias\n",
      "=> Loading blocks.5.attn.v_bias\n",
      "=> Loading blocks.5.attn.qkv.weight\n",
      "=> Loading blocks.5.attn.proj.weight\n",
      "=> Loading blocks.5.attn.proj.bias\n",
      "=> Loading blocks.5.norm2.weight\n",
      "=> Loading blocks.5.norm2.bias\n",
      "=> Loading blocks.5.mlp.fc1.weight\n",
      "=> Loading blocks.5.mlp.fc1.bias\n",
      "=> Loading blocks.5.mlp.fc2.weight\n",
      "=> Loading blocks.5.mlp.fc2.bias\n",
      "=> Loading blocks.6.gamma_1\n",
      "=> Loading blocks.6.gamma_2\n",
      "=> Loading blocks.6.norm1.weight\n",
      "=> Loading blocks.6.norm1.bias\n",
      "=> Loading blocks.6.attn.q_bias\n",
      "=> Loading blocks.6.attn.v_bias\n",
      "=> Loading blocks.6.attn.qkv.weight\n",
      "=> Loading blocks.6.attn.proj.weight\n",
      "=> Loading blocks.6.attn.proj.bias\n",
      "=> Loading blocks.6.norm2.weight\n",
      "=> Loading blocks.6.norm2.bias\n",
      "=> Loading blocks.6.mlp.fc1.weight\n",
      "=> Loading blocks.6.mlp.fc1.bias\n",
      "=> Loading blocks.6.mlp.fc2.weight\n",
      "=> Loading blocks.6.mlp.fc2.bias\n",
      "=> Loading blocks.7.gamma_1\n",
      "=> Loading blocks.7.gamma_2\n",
      "=> Loading blocks.7.norm1.weight\n",
      "=> Loading blocks.7.norm1.bias\n",
      "=> Loading blocks.7.attn.q_bias\n",
      "=> Loading blocks.7.attn.v_bias\n",
      "=> Loading blocks.7.attn.qkv.weight\n",
      "=> Loading blocks.7.attn.proj.weight\n",
      "=> Loading blocks.7.attn.proj.bias\n",
      "=> Loading blocks.7.norm2.weight\n",
      "=> Loading blocks.7.norm2.bias\n",
      "=> Loading blocks.7.mlp.fc1.weight\n",
      "=> Loading blocks.7.mlp.fc1.bias\n",
      "=> Loading blocks.7.mlp.fc2.weight\n",
      "=> Loading blocks.7.mlp.fc2.bias\n",
      "=> Loading blocks.8.gamma_1\n",
      "=> Loading blocks.8.gamma_2\n",
      "=> Loading blocks.8.norm1.weight\n",
      "=> Loading blocks.8.norm1.bias\n",
      "=> Loading blocks.8.attn.q_bias\n",
      "=> Loading blocks.8.attn.v_bias\n",
      "=> Loading blocks.8.attn.qkv.weight\n",
      "=> Loading blocks.8.attn.proj.weight\n",
      "=> Loading blocks.8.attn.proj.bias\n",
      "=> Loading blocks.8.norm2.weight\n",
      "=> Loading blocks.8.norm2.bias\n",
      "=> Loading blocks.8.mlp.fc1.weight\n",
      "=> Loading blocks.8.mlp.fc1.bias\n",
      "=> Loading blocks.8.mlp.fc2.weight\n",
      "=> Loading blocks.8.mlp.fc2.bias\n",
      "=> Loading blocks.9.gamma_1\n",
      "=> Loading blocks.9.gamma_2\n",
      "=> Loading blocks.9.norm1.weight\n",
      "=> Loading blocks.9.norm1.bias\n",
      "=> Loading blocks.9.attn.q_bias\n",
      "=> Loading blocks.9.attn.v_bias\n",
      "=> Loading blocks.9.attn.qkv.weight\n",
      "=> Loading blocks.9.attn.proj.weight\n",
      "=> Loading blocks.9.attn.proj.bias\n",
      "=> Loading blocks.9.norm2.weight\n",
      "=> Loading blocks.9.norm2.bias\n",
      "=> Loading blocks.9.mlp.fc1.weight\n",
      "=> Loading blocks.9.mlp.fc1.bias\n",
      "=> Loading blocks.9.mlp.fc2.weight\n",
      "=> Loading blocks.9.mlp.fc2.bias\n",
      "=> Loading blocks.10.gamma_1\n",
      "=> Loading blocks.10.gamma_2\n",
      "=> Loading blocks.10.norm1.weight\n",
      "=> Loading blocks.10.norm1.bias\n",
      "=> Loading blocks.10.attn.q_bias\n",
      "=> Loading blocks.10.attn.v_bias\n",
      "=> Loading blocks.10.attn.qkv.weight\n",
      "=> Loading blocks.10.attn.proj.weight\n",
      "=> Loading blocks.10.attn.proj.bias\n",
      "=> Loading blocks.10.norm2.weight\n",
      "=> Loading blocks.10.norm2.bias\n",
      "=> Loading blocks.10.mlp.fc1.weight\n",
      "=> Loading blocks.10.mlp.fc1.bias\n",
      "=> Loading blocks.10.mlp.fc2.weight\n",
      "=> Loading blocks.10.mlp.fc2.bias\n",
      "=> Loading blocks.11.gamma_1\n",
      "=> Loading blocks.11.gamma_2\n",
      "=> Loading blocks.11.norm1.weight\n",
      "=> Loading blocks.11.norm1.bias\n",
      "=> Loading blocks.11.attn.q_bias\n",
      "=> Loading blocks.11.attn.v_bias\n",
      "=> Loading blocks.11.attn.qkv.weight\n",
      "=> Loading blocks.11.attn.proj.weight\n",
      "=> Loading blocks.11.attn.proj.bias\n",
      "=> Loading blocks.11.norm2.weight\n",
      "=> Loading blocks.11.norm2.bias\n",
      "=> Loading blocks.11.mlp.fc1.weight\n",
      "=> Loading blocks.11.mlp.fc1.bias\n",
      "=> Loading blocks.11.mlp.fc2.weight\n",
      "=> Loading blocks.11.mlp.fc2.bias\n",
      "=> Loading blocks.12.gamma_1\n",
      "=> Loading blocks.12.gamma_2\n",
      "=> Loading blocks.12.norm1.weight\n",
      "=> Loading blocks.12.norm1.bias\n",
      "=> Loading blocks.12.attn.q_bias\n",
      "=> Loading blocks.12.attn.v_bias\n",
      "=> Loading blocks.12.attn.qkv.weight\n",
      "=> Loading blocks.12.attn.proj.weight\n",
      "=> Loading blocks.12.attn.proj.bias\n",
      "=> Loading blocks.12.norm2.weight\n",
      "=> Loading blocks.12.norm2.bias\n",
      "=> Loading blocks.12.mlp.fc1.weight\n",
      "=> Loading blocks.12.mlp.fc1.bias\n",
      "=> Loading blocks.12.mlp.fc2.weight\n",
      "=> Loading blocks.12.mlp.fc2.bias\n",
      "=> Loading blocks.13.gamma_1\n",
      "=> Loading blocks.13.gamma_2\n",
      "=> Loading blocks.13.norm1.weight\n",
      "=> Loading blocks.13.norm1.bias\n",
      "=> Loading blocks.13.attn.q_bias\n",
      "=> Loading blocks.13.attn.v_bias\n",
      "=> Loading blocks.13.attn.qkv.weight\n",
      "=> Loading blocks.13.attn.proj.weight\n",
      "=> Loading blocks.13.attn.proj.bias\n",
      "=> Loading blocks.13.norm2.weight\n",
      "=> Loading blocks.13.norm2.bias\n",
      "=> Loading blocks.13.mlp.fc1.weight\n",
      "=> Loading blocks.13.mlp.fc1.bias\n",
      "=> Loading blocks.13.mlp.fc2.weight\n",
      "=> Loading blocks.13.mlp.fc2.bias\n",
      "=> Loading blocks.14.gamma_1\n",
      "=> Loading blocks.14.gamma_2\n",
      "=> Loading blocks.14.norm1.weight\n",
      "=> Loading blocks.14.norm1.bias\n",
      "=> Loading blocks.14.attn.q_bias\n",
      "=> Loading blocks.14.attn.v_bias\n",
      "=> Loading blocks.14.attn.qkv.weight\n",
      "=> Loading blocks.14.attn.proj.weight\n",
      "=> Loading blocks.14.attn.proj.bias\n",
      "=> Loading blocks.14.norm2.weight\n",
      "=> Loading blocks.14.norm2.bias\n",
      "=> Loading blocks.14.mlp.fc1.weight\n",
      "=> Loading blocks.14.mlp.fc1.bias\n",
      "=> Loading blocks.14.mlp.fc2.weight\n",
      "=> Loading blocks.14.mlp.fc2.bias\n",
      "=> Loading blocks.15.gamma_1\n",
      "=> Loading blocks.15.gamma_2\n",
      "=> Loading blocks.15.norm1.weight\n",
      "=> Loading blocks.15.norm1.bias\n",
      "=> Loading blocks.15.attn.q_bias\n",
      "=> Loading blocks.15.attn.v_bias\n",
      "=> Loading blocks.15.attn.qkv.weight\n",
      "=> Loading blocks.15.attn.proj.weight\n",
      "=> Loading blocks.15.attn.proj.bias\n",
      "=> Loading blocks.15.norm2.weight\n",
      "=> Loading blocks.15.norm2.bias\n",
      "=> Loading blocks.15.mlp.fc1.weight\n",
      "=> Loading blocks.15.mlp.fc1.bias\n",
      "=> Loading blocks.15.mlp.fc2.weight\n",
      "=> Loading blocks.15.mlp.fc2.bias\n",
      "=> Loading blocks.16.gamma_1\n",
      "=> Loading blocks.16.gamma_2\n",
      "=> Loading blocks.16.norm1.weight\n",
      "=> Loading blocks.16.norm1.bias\n",
      "=> Loading blocks.16.attn.q_bias\n",
      "=> Loading blocks.16.attn.v_bias\n",
      "=> Loading blocks.16.attn.qkv.weight\n",
      "=> Loading blocks.16.attn.proj.weight\n",
      "=> Loading blocks.16.attn.proj.bias\n",
      "=> Loading blocks.16.norm2.weight\n",
      "=> Loading blocks.16.norm2.bias\n",
      "=> Loading blocks.16.mlp.fc1.weight\n",
      "=> Loading blocks.16.mlp.fc1.bias\n",
      "=> Loading blocks.16.mlp.fc2.weight\n",
      "=> Loading blocks.16.mlp.fc2.bias\n",
      "=> Loading blocks.17.gamma_1\n",
      "=> Loading blocks.17.gamma_2\n",
      "=> Loading blocks.17.norm1.weight\n",
      "=> Loading blocks.17.norm1.bias\n",
      "=> Loading blocks.17.attn.q_bias\n",
      "=> Loading blocks.17.attn.v_bias\n",
      "=> Loading blocks.17.attn.qkv.weight\n",
      "=> Loading blocks.17.attn.proj.weight\n",
      "=> Loading blocks.17.attn.proj.bias\n",
      "=> Loading blocks.17.norm2.weight\n",
      "=> Loading blocks.17.norm2.bias\n",
      "=> Loading blocks.17.mlp.fc1.weight\n",
      "=> Loading blocks.17.mlp.fc1.bias\n",
      "=> Loading blocks.17.mlp.fc2.weight\n",
      "=> Loading blocks.17.mlp.fc2.bias\n",
      "=> Loading blocks.18.gamma_1\n",
      "=> Loading blocks.18.gamma_2\n",
      "=> Loading blocks.18.norm1.weight\n",
      "=> Loading blocks.18.norm1.bias\n",
      "=> Loading blocks.18.attn.q_bias\n",
      "=> Loading blocks.18.attn.v_bias\n",
      "=> Loading blocks.18.attn.qkv.weight\n",
      "=> Loading blocks.18.attn.proj.weight\n",
      "=> Loading blocks.18.attn.proj.bias\n",
      "=> Loading blocks.18.norm2.weight\n",
      "=> Loading blocks.18.norm2.bias\n",
      "=> Loading blocks.18.mlp.fc1.weight\n",
      "=> Loading blocks.18.mlp.fc1.bias\n",
      "=> Loading blocks.18.mlp.fc2.weight\n",
      "=> Loading blocks.18.mlp.fc2.bias\n",
      "=> Loading blocks.19.gamma_1\n",
      "=> Loading blocks.19.gamma_2\n",
      "=> Loading blocks.19.norm1.weight\n",
      "=> Loading blocks.19.norm1.bias\n",
      "=> Loading blocks.19.attn.q_bias\n",
      "=> Loading blocks.19.attn.v_bias\n",
      "=> Loading blocks.19.attn.qkv.weight\n",
      "=> Loading blocks.19.attn.proj.weight\n",
      "=> Loading blocks.19.attn.proj.bias\n",
      "=> Loading blocks.19.norm2.weight\n",
      "=> Loading blocks.19.norm2.bias\n",
      "=> Loading blocks.19.mlp.fc1.weight\n",
      "=> Loading blocks.19.mlp.fc1.bias\n",
      "=> Loading blocks.19.mlp.fc2.weight\n",
      "=> Loading blocks.19.mlp.fc2.bias\n",
      "=> Loading blocks.20.gamma_1\n",
      "=> Loading blocks.20.gamma_2\n",
      "=> Loading blocks.20.norm1.weight\n",
      "=> Loading blocks.20.norm1.bias\n",
      "=> Loading blocks.20.attn.q_bias\n",
      "=> Loading blocks.20.attn.v_bias\n",
      "=> Loading blocks.20.attn.qkv.weight\n",
      "=> Loading blocks.20.attn.proj.weight\n",
      "=> Loading blocks.20.attn.proj.bias\n",
      "=> Loading blocks.20.norm2.weight\n",
      "=> Loading blocks.20.norm2.bias\n",
      "=> Loading blocks.20.mlp.fc1.weight\n",
      "=> Loading blocks.20.mlp.fc1.bias\n",
      "=> Loading blocks.20.mlp.fc2.weight\n",
      "=> Loading blocks.20.mlp.fc2.bias\n",
      "=> Loading blocks.21.gamma_1\n",
      "=> Loading blocks.21.gamma_2\n",
      "=> Loading blocks.21.norm1.weight\n",
      "=> Loading blocks.21.norm1.bias\n",
      "=> Loading blocks.21.attn.q_bias\n",
      "=> Loading blocks.21.attn.v_bias\n",
      "=> Loading blocks.21.attn.qkv.weight\n",
      "=> Loading blocks.21.attn.proj.weight\n",
      "=> Loading blocks.21.attn.proj.bias\n",
      "=> Loading blocks.21.norm2.weight\n",
      "=> Loading blocks.21.norm2.bias\n",
      "=> Loading blocks.21.mlp.fc1.weight\n",
      "=> Loading blocks.21.mlp.fc1.bias\n",
      "=> Loading blocks.21.mlp.fc2.weight\n",
      "=> Loading blocks.21.mlp.fc2.bias\n",
      "=> Loading blocks.22.gamma_1\n",
      "=> Loading blocks.22.gamma_2\n",
      "=> Loading blocks.22.norm1.weight\n",
      "=> Loading blocks.22.norm1.bias\n",
      "=> Loading blocks.22.attn.q_bias\n",
      "=> Loading blocks.22.attn.v_bias\n",
      "=> Loading blocks.22.attn.qkv.weight\n",
      "=> Loading blocks.22.attn.proj.weight\n",
      "=> Loading blocks.22.attn.proj.bias\n",
      "=> Loading blocks.22.norm2.weight\n",
      "=> Loading blocks.22.norm2.bias\n",
      "=> Loading blocks.22.mlp.fc1.weight\n",
      "=> Loading blocks.22.mlp.fc1.bias\n",
      "=> Loading blocks.22.mlp.fc2.weight\n",
      "=> Loading blocks.22.mlp.fc2.bias\n",
      "=> Loading blocks.23.gamma_1\n",
      "=> Loading blocks.23.gamma_2\n",
      "=> Loading blocks.23.norm1.weight\n",
      "=> Loading blocks.23.norm1.bias\n",
      "=> Loading blocks.23.attn.q_bias\n",
      "=> Loading blocks.23.attn.v_bias\n",
      "=> Loading blocks.23.attn.qkv.weight\n",
      "=> Loading blocks.23.attn.proj.weight\n",
      "=> Loading blocks.23.attn.proj.bias\n",
      "=> Loading blocks.23.norm2.weight\n",
      "=> Loading blocks.23.norm2.bias\n",
      "=> Loading blocks.23.mlp.fc1.weight\n",
      "=> Loading blocks.23.mlp.fc1.bias\n",
      "=> Loading blocks.23.mlp.fc2.weight\n",
      "=> Loading blocks.23.mlp.fc2.bias\n",
      "=> Loading norm.weight\n",
      "=> Loading norm.bias\n",
      "=> CAE weights loaded\n",
      "Model dict:  odict_keys(['segmentor.backbone.cls_token', 'segmentor.backbone.pos_embed', 'segmentor.backbone.patch_embed.proj.weight', 'segmentor.backbone.patch_embed.proj.bias', 'segmentor.backbone.blocks.0.gamma_1', 'segmentor.backbone.blocks.0.gamma_2', 'segmentor.backbone.blocks.0.norm1.weight', 'segmentor.backbone.blocks.0.norm1.bias', 'segmentor.backbone.blocks.0.attn.q_bias', 'segmentor.backbone.blocks.0.attn.v_bias', 'segmentor.backbone.blocks.0.attn.qkv.weight', 'segmentor.backbone.blocks.0.attn.proj.weight', 'segmentor.backbone.blocks.0.attn.proj.bias', 'segmentor.backbone.blocks.0.norm2.weight', 'segmentor.backbone.blocks.0.norm2.bias', 'segmentor.backbone.blocks.0.mlp.fc1.weight', 'segmentor.backbone.blocks.0.mlp.fc1.bias', 'segmentor.backbone.blocks.0.mlp.fc2.weight', 'segmentor.backbone.blocks.0.mlp.fc2.bias', 'segmentor.backbone.blocks.1.gamma_1', 'segmentor.backbone.blocks.1.gamma_2', 'segmentor.backbone.blocks.1.norm1.weight', 'segmentor.backbone.blocks.1.norm1.bias', 'segmentor.backbone.blocks.1.attn.q_bias', 'segmentor.backbone.blocks.1.attn.v_bias', 'segmentor.backbone.blocks.1.attn.qkv.weight', 'segmentor.backbone.blocks.1.attn.proj.weight', 'segmentor.backbone.blocks.1.attn.proj.bias', 'segmentor.backbone.blocks.1.norm2.weight', 'segmentor.backbone.blocks.1.norm2.bias', 'segmentor.backbone.blocks.1.mlp.fc1.weight', 'segmentor.backbone.blocks.1.mlp.fc1.bias', 'segmentor.backbone.blocks.1.mlp.fc2.weight', 'segmentor.backbone.blocks.1.mlp.fc2.bias', 'segmentor.backbone.blocks.2.gamma_1', 'segmentor.backbone.blocks.2.gamma_2', 'segmentor.backbone.blocks.2.norm1.weight', 'segmentor.backbone.blocks.2.norm1.bias', 'segmentor.backbone.blocks.2.attn.q_bias', 'segmentor.backbone.blocks.2.attn.v_bias', 'segmentor.backbone.blocks.2.attn.qkv.weight', 'segmentor.backbone.blocks.2.attn.proj.weight', 'segmentor.backbone.blocks.2.attn.proj.bias', 'segmentor.backbone.blocks.2.norm2.weight', 'segmentor.backbone.blocks.2.norm2.bias', 'segmentor.backbone.blocks.2.mlp.fc1.weight', 'segmentor.backbone.blocks.2.mlp.fc1.bias', 'segmentor.backbone.blocks.2.mlp.fc2.weight', 'segmentor.backbone.blocks.2.mlp.fc2.bias', 'segmentor.backbone.blocks.3.gamma_1', 'segmentor.backbone.blocks.3.gamma_2', 'segmentor.backbone.blocks.3.norm1.weight', 'segmentor.backbone.blocks.3.norm1.bias', 'segmentor.backbone.blocks.3.attn.q_bias', 'segmentor.backbone.blocks.3.attn.v_bias', 'segmentor.backbone.blocks.3.attn.qkv.weight', 'segmentor.backbone.blocks.3.attn.proj.weight', 'segmentor.backbone.blocks.3.attn.proj.bias', 'segmentor.backbone.blocks.3.norm2.weight', 'segmentor.backbone.blocks.3.norm2.bias', 'segmentor.backbone.blocks.3.mlp.fc1.weight', 'segmentor.backbone.blocks.3.mlp.fc1.bias', 'segmentor.backbone.blocks.3.mlp.fc2.weight', 'segmentor.backbone.blocks.3.mlp.fc2.bias', 'segmentor.backbone.blocks.4.gamma_1', 'segmentor.backbone.blocks.4.gamma_2', 'segmentor.backbone.blocks.4.norm1.weight', 'segmentor.backbone.blocks.4.norm1.bias', 'segmentor.backbone.blocks.4.attn.q_bias', 'segmentor.backbone.blocks.4.attn.v_bias', 'segmentor.backbone.blocks.4.attn.qkv.weight', 'segmentor.backbone.blocks.4.attn.proj.weight', 'segmentor.backbone.blocks.4.attn.proj.bias', 'segmentor.backbone.blocks.4.norm2.weight', 'segmentor.backbone.blocks.4.norm2.bias', 'segmentor.backbone.blocks.4.mlp.fc1.weight', 'segmentor.backbone.blocks.4.mlp.fc1.bias', 'segmentor.backbone.blocks.4.mlp.fc2.weight', 'segmentor.backbone.blocks.4.mlp.fc2.bias', 'segmentor.backbone.blocks.5.gamma_1', 'segmentor.backbone.blocks.5.gamma_2', 'segmentor.backbone.blocks.5.norm1.weight', 'segmentor.backbone.blocks.5.norm1.bias', 'segmentor.backbone.blocks.5.attn.q_bias', 'segmentor.backbone.blocks.5.attn.v_bias', 'segmentor.backbone.blocks.5.attn.qkv.weight', 'segmentor.backbone.blocks.5.attn.proj.weight', 'segmentor.backbone.blocks.5.attn.proj.bias', 'segmentor.backbone.blocks.5.norm2.weight', 'segmentor.backbone.blocks.5.norm2.bias', 'segmentor.backbone.blocks.5.mlp.fc1.weight', 'segmentor.backbone.blocks.5.mlp.fc1.bias', 'segmentor.backbone.blocks.5.mlp.fc2.weight', 'segmentor.backbone.blocks.5.mlp.fc2.bias', 'segmentor.backbone.blocks.6.gamma_1', 'segmentor.backbone.blocks.6.gamma_2', 'segmentor.backbone.blocks.6.norm1.weight', 'segmentor.backbone.blocks.6.norm1.bias', 'segmentor.backbone.blocks.6.attn.q_bias', 'segmentor.backbone.blocks.6.attn.v_bias', 'segmentor.backbone.blocks.6.attn.qkv.weight', 'segmentor.backbone.blocks.6.attn.proj.weight', 'segmentor.backbone.blocks.6.attn.proj.bias', 'segmentor.backbone.blocks.6.norm2.weight', 'segmentor.backbone.blocks.6.norm2.bias', 'segmentor.backbone.blocks.6.mlp.fc1.weight', 'segmentor.backbone.blocks.6.mlp.fc1.bias', 'segmentor.backbone.blocks.6.mlp.fc2.weight', 'segmentor.backbone.blocks.6.mlp.fc2.bias', 'segmentor.backbone.blocks.7.gamma_1', 'segmentor.backbone.blocks.7.gamma_2', 'segmentor.backbone.blocks.7.norm1.weight', 'segmentor.backbone.blocks.7.norm1.bias', 'segmentor.backbone.blocks.7.attn.q_bias', 'segmentor.backbone.blocks.7.attn.v_bias', 'segmentor.backbone.blocks.7.attn.qkv.weight', 'segmentor.backbone.blocks.7.attn.proj.weight', 'segmentor.backbone.blocks.7.attn.proj.bias', 'segmentor.backbone.blocks.7.norm2.weight', 'segmentor.backbone.blocks.7.norm2.bias', 'segmentor.backbone.blocks.7.mlp.fc1.weight', 'segmentor.backbone.blocks.7.mlp.fc1.bias', 'segmentor.backbone.blocks.7.mlp.fc2.weight', 'segmentor.backbone.blocks.7.mlp.fc2.bias', 'segmentor.backbone.blocks.8.gamma_1', 'segmentor.backbone.blocks.8.gamma_2', 'segmentor.backbone.blocks.8.norm1.weight', 'segmentor.backbone.blocks.8.norm1.bias', 'segmentor.backbone.blocks.8.attn.q_bias', 'segmentor.backbone.blocks.8.attn.v_bias', 'segmentor.backbone.blocks.8.attn.qkv.weight', 'segmentor.backbone.blocks.8.attn.proj.weight', 'segmentor.backbone.blocks.8.attn.proj.bias', 'segmentor.backbone.blocks.8.norm2.weight', 'segmentor.backbone.blocks.8.norm2.bias', 'segmentor.backbone.blocks.8.mlp.fc1.weight', 'segmentor.backbone.blocks.8.mlp.fc1.bias', 'segmentor.backbone.blocks.8.mlp.fc2.weight', 'segmentor.backbone.blocks.8.mlp.fc2.bias', 'segmentor.backbone.blocks.9.gamma_1', 'segmentor.backbone.blocks.9.gamma_2', 'segmentor.backbone.blocks.9.norm1.weight', 'segmentor.backbone.blocks.9.norm1.bias', 'segmentor.backbone.blocks.9.attn.q_bias', 'segmentor.backbone.blocks.9.attn.v_bias', 'segmentor.backbone.blocks.9.attn.qkv.weight', 'segmentor.backbone.blocks.9.attn.proj.weight', 'segmentor.backbone.blocks.9.attn.proj.bias', 'segmentor.backbone.blocks.9.norm2.weight', 'segmentor.backbone.blocks.9.norm2.bias', 'segmentor.backbone.blocks.9.mlp.fc1.weight', 'segmentor.backbone.blocks.9.mlp.fc1.bias', 'segmentor.backbone.blocks.9.mlp.fc2.weight', 'segmentor.backbone.blocks.9.mlp.fc2.bias', 'segmentor.backbone.blocks.10.gamma_1', 'segmentor.backbone.blocks.10.gamma_2', 'segmentor.backbone.blocks.10.norm1.weight', 'segmentor.backbone.blocks.10.norm1.bias', 'segmentor.backbone.blocks.10.attn.q_bias', 'segmentor.backbone.blocks.10.attn.v_bias', 'segmentor.backbone.blocks.10.attn.qkv.weight', 'segmentor.backbone.blocks.10.attn.proj.weight', 'segmentor.backbone.blocks.10.attn.proj.bias', 'segmentor.backbone.blocks.10.norm2.weight', 'segmentor.backbone.blocks.10.norm2.bias', 'segmentor.backbone.blocks.10.mlp.fc1.weight', 'segmentor.backbone.blocks.10.mlp.fc1.bias', 'segmentor.backbone.blocks.10.mlp.fc2.weight', 'segmentor.backbone.blocks.10.mlp.fc2.bias', 'segmentor.backbone.blocks.11.gamma_1', 'segmentor.backbone.blocks.11.gamma_2', 'segmentor.backbone.blocks.11.norm1.weight', 'segmentor.backbone.blocks.11.norm1.bias', 'segmentor.backbone.blocks.11.attn.q_bias', 'segmentor.backbone.blocks.11.attn.v_bias', 'segmentor.backbone.blocks.11.attn.qkv.weight', 'segmentor.backbone.blocks.11.attn.proj.weight', 'segmentor.backbone.blocks.11.attn.proj.bias', 'segmentor.backbone.blocks.11.norm2.weight', 'segmentor.backbone.blocks.11.norm2.bias', 'segmentor.backbone.blocks.11.mlp.fc1.weight', 'segmentor.backbone.blocks.11.mlp.fc1.bias', 'segmentor.backbone.blocks.11.mlp.fc2.weight', 'segmentor.backbone.blocks.11.mlp.fc2.bias', 'segmentor.backbone.blocks.12.gamma_1', 'segmentor.backbone.blocks.12.gamma_2', 'segmentor.backbone.blocks.12.norm1.weight', 'segmentor.backbone.blocks.12.norm1.bias', 'segmentor.backbone.blocks.12.attn.q_bias', 'segmentor.backbone.blocks.12.attn.v_bias', 'segmentor.backbone.blocks.12.attn.qkv.weight', 'segmentor.backbone.blocks.12.attn.proj.weight', 'segmentor.backbone.blocks.12.attn.proj.bias', 'segmentor.backbone.blocks.12.norm2.weight', 'segmentor.backbone.blocks.12.norm2.bias', 'segmentor.backbone.blocks.12.mlp.fc1.weight', 'segmentor.backbone.blocks.12.mlp.fc1.bias', 'segmentor.backbone.blocks.12.mlp.fc2.weight', 'segmentor.backbone.blocks.12.mlp.fc2.bias', 'segmentor.backbone.blocks.13.gamma_1', 'segmentor.backbone.blocks.13.gamma_2', 'segmentor.backbone.blocks.13.norm1.weight', 'segmentor.backbone.blocks.13.norm1.bias', 'segmentor.backbone.blocks.13.attn.q_bias', 'segmentor.backbone.blocks.13.attn.v_bias', 'segmentor.backbone.blocks.13.attn.qkv.weight', 'segmentor.backbone.blocks.13.attn.proj.weight', 'segmentor.backbone.blocks.13.attn.proj.bias', 'segmentor.backbone.blocks.13.norm2.weight', 'segmentor.backbone.blocks.13.norm2.bias', 'segmentor.backbone.blocks.13.mlp.fc1.weight', 'segmentor.backbone.blocks.13.mlp.fc1.bias', 'segmentor.backbone.blocks.13.mlp.fc2.weight', 'segmentor.backbone.blocks.13.mlp.fc2.bias', 'segmentor.backbone.blocks.14.gamma_1', 'segmentor.backbone.blocks.14.gamma_2', 'segmentor.backbone.blocks.14.norm1.weight', 'segmentor.backbone.blocks.14.norm1.bias', 'segmentor.backbone.blocks.14.attn.q_bias', 'segmentor.backbone.blocks.14.attn.v_bias', 'segmentor.backbone.blocks.14.attn.qkv.weight', 'segmentor.backbone.blocks.14.attn.proj.weight', 'segmentor.backbone.blocks.14.attn.proj.bias', 'segmentor.backbone.blocks.14.norm2.weight', 'segmentor.backbone.blocks.14.norm2.bias', 'segmentor.backbone.blocks.14.mlp.fc1.weight', 'segmentor.backbone.blocks.14.mlp.fc1.bias', 'segmentor.backbone.blocks.14.mlp.fc2.weight', 'segmentor.backbone.blocks.14.mlp.fc2.bias', 'segmentor.backbone.blocks.15.gamma_1', 'segmentor.backbone.blocks.15.gamma_2', 'segmentor.backbone.blocks.15.norm1.weight', 'segmentor.backbone.blocks.15.norm1.bias', 'segmentor.backbone.blocks.15.attn.q_bias', 'segmentor.backbone.blocks.15.attn.v_bias', 'segmentor.backbone.blocks.15.attn.qkv.weight', 'segmentor.backbone.blocks.15.attn.proj.weight', 'segmentor.backbone.blocks.15.attn.proj.bias', 'segmentor.backbone.blocks.15.norm2.weight', 'segmentor.backbone.blocks.15.norm2.bias', 'segmentor.backbone.blocks.15.mlp.fc1.weight', 'segmentor.backbone.blocks.15.mlp.fc1.bias', 'segmentor.backbone.blocks.15.mlp.fc2.weight', 'segmentor.backbone.blocks.15.mlp.fc2.bias', 'segmentor.backbone.blocks.16.gamma_1', 'segmentor.backbone.blocks.16.gamma_2', 'segmentor.backbone.blocks.16.norm1.weight', 'segmentor.backbone.blocks.16.norm1.bias', 'segmentor.backbone.blocks.16.attn.q_bias', 'segmentor.backbone.blocks.16.attn.v_bias', 'segmentor.backbone.blocks.16.attn.qkv.weight', 'segmentor.backbone.blocks.16.attn.proj.weight', 'segmentor.backbone.blocks.16.attn.proj.bias', 'segmentor.backbone.blocks.16.norm2.weight', 'segmentor.backbone.blocks.16.norm2.bias', 'segmentor.backbone.blocks.16.mlp.fc1.weight', 'segmentor.backbone.blocks.16.mlp.fc1.bias', 'segmentor.backbone.blocks.16.mlp.fc2.weight', 'segmentor.backbone.blocks.16.mlp.fc2.bias', 'segmentor.backbone.blocks.17.gamma_1', 'segmentor.backbone.blocks.17.gamma_2', 'segmentor.backbone.blocks.17.norm1.weight', 'segmentor.backbone.blocks.17.norm1.bias', 'segmentor.backbone.blocks.17.attn.q_bias', 'segmentor.backbone.blocks.17.attn.v_bias', 'segmentor.backbone.blocks.17.attn.qkv.weight', 'segmentor.backbone.blocks.17.attn.proj.weight', 'segmentor.backbone.blocks.17.attn.proj.bias', 'segmentor.backbone.blocks.17.norm2.weight', 'segmentor.backbone.blocks.17.norm2.bias', 'segmentor.backbone.blocks.17.mlp.fc1.weight', 'segmentor.backbone.blocks.17.mlp.fc1.bias', 'segmentor.backbone.blocks.17.mlp.fc2.weight', 'segmentor.backbone.blocks.17.mlp.fc2.bias', 'segmentor.backbone.blocks.18.gamma_1', 'segmentor.backbone.blocks.18.gamma_2', 'segmentor.backbone.blocks.18.norm1.weight', 'segmentor.backbone.blocks.18.norm1.bias', 'segmentor.backbone.blocks.18.attn.q_bias', 'segmentor.backbone.blocks.18.attn.v_bias', 'segmentor.backbone.blocks.18.attn.qkv.weight', 'segmentor.backbone.blocks.18.attn.proj.weight', 'segmentor.backbone.blocks.18.attn.proj.bias', 'segmentor.backbone.blocks.18.norm2.weight', 'segmentor.backbone.blocks.18.norm2.bias', 'segmentor.backbone.blocks.18.mlp.fc1.weight', 'segmentor.backbone.blocks.18.mlp.fc1.bias', 'segmentor.backbone.blocks.18.mlp.fc2.weight', 'segmentor.backbone.blocks.18.mlp.fc2.bias', 'segmentor.backbone.blocks.19.gamma_1', 'segmentor.backbone.blocks.19.gamma_2', 'segmentor.backbone.blocks.19.norm1.weight', 'segmentor.backbone.blocks.19.norm1.bias', 'segmentor.backbone.blocks.19.attn.q_bias', 'segmentor.backbone.blocks.19.attn.v_bias', 'segmentor.backbone.blocks.19.attn.qkv.weight', 'segmentor.backbone.blocks.19.attn.proj.weight', 'segmentor.backbone.blocks.19.attn.proj.bias', 'segmentor.backbone.blocks.19.norm2.weight', 'segmentor.backbone.blocks.19.norm2.bias', 'segmentor.backbone.blocks.19.mlp.fc1.weight', 'segmentor.backbone.blocks.19.mlp.fc1.bias', 'segmentor.backbone.blocks.19.mlp.fc2.weight', 'segmentor.backbone.blocks.19.mlp.fc2.bias', 'segmentor.backbone.blocks.20.gamma_1', 'segmentor.backbone.blocks.20.gamma_2', 'segmentor.backbone.blocks.20.norm1.weight', 'segmentor.backbone.blocks.20.norm1.bias', 'segmentor.backbone.blocks.20.attn.q_bias', 'segmentor.backbone.blocks.20.attn.v_bias', 'segmentor.backbone.blocks.20.attn.qkv.weight', 'segmentor.backbone.blocks.20.attn.proj.weight', 'segmentor.backbone.blocks.20.attn.proj.bias', 'segmentor.backbone.blocks.20.norm2.weight', 'segmentor.backbone.blocks.20.norm2.bias', 'segmentor.backbone.blocks.20.mlp.fc1.weight', 'segmentor.backbone.blocks.20.mlp.fc1.bias', 'segmentor.backbone.blocks.20.mlp.fc2.weight', 'segmentor.backbone.blocks.20.mlp.fc2.bias', 'segmentor.backbone.blocks.21.gamma_1', 'segmentor.backbone.blocks.21.gamma_2', 'segmentor.backbone.blocks.21.norm1.weight', 'segmentor.backbone.blocks.21.norm1.bias', 'segmentor.backbone.blocks.21.attn.q_bias', 'segmentor.backbone.blocks.21.attn.v_bias', 'segmentor.backbone.blocks.21.attn.qkv.weight', 'segmentor.backbone.blocks.21.attn.proj.weight', 'segmentor.backbone.blocks.21.attn.proj.bias', 'segmentor.backbone.blocks.21.norm2.weight', 'segmentor.backbone.blocks.21.norm2.bias', 'segmentor.backbone.blocks.21.mlp.fc1.weight', 'segmentor.backbone.blocks.21.mlp.fc1.bias', 'segmentor.backbone.blocks.21.mlp.fc2.weight', 'segmentor.backbone.blocks.21.mlp.fc2.bias', 'segmentor.backbone.blocks.22.gamma_1', 'segmentor.backbone.blocks.22.gamma_2', 'segmentor.backbone.blocks.22.norm1.weight', 'segmentor.backbone.blocks.22.norm1.bias', 'segmentor.backbone.blocks.22.attn.q_bias', 'segmentor.backbone.blocks.22.attn.v_bias', 'segmentor.backbone.blocks.22.attn.qkv.weight', 'segmentor.backbone.blocks.22.attn.proj.weight', 'segmentor.backbone.blocks.22.attn.proj.bias', 'segmentor.backbone.blocks.22.norm2.weight', 'segmentor.backbone.blocks.22.norm2.bias', 'segmentor.backbone.blocks.22.mlp.fc1.weight', 'segmentor.backbone.blocks.22.mlp.fc1.bias', 'segmentor.backbone.blocks.22.mlp.fc2.weight', 'segmentor.backbone.blocks.22.mlp.fc2.bias', 'segmentor.backbone.blocks.23.gamma_1', 'segmentor.backbone.blocks.23.gamma_2', 'segmentor.backbone.blocks.23.norm1.weight', 'segmentor.backbone.blocks.23.norm1.bias', 'segmentor.backbone.blocks.23.attn.q_bias', 'segmentor.backbone.blocks.23.attn.v_bias', 'segmentor.backbone.blocks.23.attn.qkv.weight', 'segmentor.backbone.blocks.23.attn.proj.weight', 'segmentor.backbone.blocks.23.attn.proj.bias', 'segmentor.backbone.blocks.23.norm2.weight', 'segmentor.backbone.blocks.23.norm2.bias', 'segmentor.backbone.blocks.23.mlp.fc1.weight', 'segmentor.backbone.blocks.23.mlp.fc1.bias', 'segmentor.backbone.blocks.23.mlp.fc2.weight', 'segmentor.backbone.blocks.23.mlp.fc2.bias', 'segmentor.backbone.fpn1.0.weight', 'segmentor.backbone.fpn1.0.bias', 'segmentor.backbone.fpn1.1.weight', 'segmentor.backbone.fpn1.1.bias', 'segmentor.backbone.fpn1.1.running_mean', 'segmentor.backbone.fpn1.1.running_var', 'segmentor.backbone.fpn1.1.num_batches_tracked', 'segmentor.backbone.fpn1.3.weight', 'segmentor.backbone.fpn1.3.bias', 'segmentor.backbone.fpn2.0.weight', 'segmentor.backbone.fpn2.0.bias', 'segmentor.backbone.norm.weight', 'segmentor.backbone.norm.bias', 'segmentor.decode_head.conv_seg.weight', 'segmentor.decode_head.conv_seg.bias', 'segmentor.decode_head.psp_modules.0.1.conv.weight', 'segmentor.decode_head.psp_modules.0.1.bn.weight', 'segmentor.decode_head.psp_modules.0.1.bn.bias', 'segmentor.decode_head.psp_modules.0.1.bn.running_mean', 'segmentor.decode_head.psp_modules.0.1.bn.running_var', 'segmentor.decode_head.psp_modules.0.1.bn.num_batches_tracked', 'segmentor.decode_head.psp_modules.1.1.conv.weight', 'segmentor.decode_head.psp_modules.1.1.bn.weight', 'segmentor.decode_head.psp_modules.1.1.bn.bias', 'segmentor.decode_head.psp_modules.1.1.bn.running_mean', 'segmentor.decode_head.psp_modules.1.1.bn.running_var', 'segmentor.decode_head.psp_modules.1.1.bn.num_batches_tracked', 'segmentor.decode_head.psp_modules.2.1.conv.weight', 'segmentor.decode_head.psp_modules.2.1.bn.weight', 'segmentor.decode_head.psp_modules.2.1.bn.bias', 'segmentor.decode_head.psp_modules.2.1.bn.running_mean', 'segmentor.decode_head.psp_modules.2.1.bn.running_var', 'segmentor.decode_head.psp_modules.2.1.bn.num_batches_tracked', 'segmentor.decode_head.psp_modules.3.1.conv.weight', 'segmentor.decode_head.psp_modules.3.1.bn.weight', 'segmentor.decode_head.psp_modules.3.1.bn.bias', 'segmentor.decode_head.psp_modules.3.1.bn.running_mean', 'segmentor.decode_head.psp_modules.3.1.bn.running_var', 'segmentor.decode_head.psp_modules.3.1.bn.num_batches_tracked', 'segmentor.decode_head.bottleneck.conv.weight', 'segmentor.decode_head.bottleneck.bn.weight', 'segmentor.decode_head.bottleneck.bn.bias', 'segmentor.decode_head.bottleneck.bn.running_mean', 'segmentor.decode_head.bottleneck.bn.running_var', 'segmentor.decode_head.bottleneck.bn.num_batches_tracked', 'segmentor.decode_head.lateral_convs.0.conv.weight', 'segmentor.decode_head.lateral_convs.0.bn.weight', 'segmentor.decode_head.lateral_convs.0.bn.bias', 'segmentor.decode_head.lateral_convs.0.bn.running_mean', 'segmentor.decode_head.lateral_convs.0.bn.running_var', 'segmentor.decode_head.lateral_convs.0.bn.num_batches_tracked', 'segmentor.decode_head.lateral_convs.1.conv.weight', 'segmentor.decode_head.lateral_convs.1.bn.weight', 'segmentor.decode_head.lateral_convs.1.bn.bias', 'segmentor.decode_head.lateral_convs.1.bn.running_mean', 'segmentor.decode_head.lateral_convs.1.bn.running_var', 'segmentor.decode_head.lateral_convs.1.bn.num_batches_tracked', 'segmentor.decode_head.lateral_convs.2.conv.weight', 'segmentor.decode_head.lateral_convs.2.bn.weight', 'segmentor.decode_head.lateral_convs.2.bn.bias', 'segmentor.decode_head.lateral_convs.2.bn.running_mean', 'segmentor.decode_head.lateral_convs.2.bn.running_var', 'segmentor.decode_head.lateral_convs.2.bn.num_batches_tracked', 'segmentor.decode_head.fpn_convs.0.conv.weight', 'segmentor.decode_head.fpn_convs.0.bn.weight', 'segmentor.decode_head.fpn_convs.0.bn.bias', 'segmentor.decode_head.fpn_convs.0.bn.running_mean', 'segmentor.decode_head.fpn_convs.0.bn.running_var', 'segmentor.decode_head.fpn_convs.0.bn.num_batches_tracked', 'segmentor.decode_head.fpn_convs.1.conv.weight', 'segmentor.decode_head.fpn_convs.1.bn.weight', 'segmentor.decode_head.fpn_convs.1.bn.bias', 'segmentor.decode_head.fpn_convs.1.bn.running_mean', 'segmentor.decode_head.fpn_convs.1.bn.running_var', 'segmentor.decode_head.fpn_convs.1.bn.num_batches_tracked', 'segmentor.decode_head.fpn_convs.2.conv.weight', 'segmentor.decode_head.fpn_convs.2.bn.weight', 'segmentor.decode_head.fpn_convs.2.bn.bias', 'segmentor.decode_head.fpn_convs.2.bn.running_mean', 'segmentor.decode_head.fpn_convs.2.bn.running_var', 'segmentor.decode_head.fpn_convs.2.bn.num_batches_tracked', 'segmentor.decode_head.fpn_bottleneck.conv.weight', 'segmentor.decode_head.fpn_bottleneck.bn.weight', 'segmentor.decode_head.fpn_bottleneck.bn.bias', 'segmentor.decode_head.fpn_bottleneck.bn.running_mean', 'segmentor.decode_head.fpn_bottleneck.bn.running_var', 'segmentor.decode_head.fpn_bottleneck.bn.num_batches_tracked'])\n",
      "Pretrained dict:  model.segmentor.backbone.cls_token\n",
      "Pretrained dict:  model.segmentor.backbone.pos_embed\n",
      "Pretrained dict:  model.segmentor.backbone.patch_embed.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.patch_embed.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.0.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.1.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.2.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.3.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.4.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.5.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.6.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.7.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.8.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.9.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.10.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.11.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.12.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.13.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.14.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.15.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.16.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.17.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.18.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.19.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.20.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.21.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.22.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.gamma_1\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.gamma_2\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.norm1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.norm1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.attn.q_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.attn.v_bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.attn.qkv.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.attn.proj.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.attn.proj.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.norm2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.norm2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.mlp.fc1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.mlp.fc1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.mlp.fc2.weight\n",
      "Pretrained dict:  model.segmentor.backbone.blocks.23.mlp.fc2.bias\n",
      "Pretrained dict:  model.segmentor.backbone.fpn1.0.weight\n",
      "Pretrained dict:  model.segmentor.backbone.fpn1.0.bias\n",
      "Pretrained dict:  model.segmentor.backbone.fpn1.1.weight\n",
      "Pretrained dict:  model.segmentor.backbone.fpn1.1.bias\n",
      "Pretrained dict:  model.segmentor.backbone.fpn1.1.running_mean\n",
      "Pretrained dict:  model.segmentor.backbone.fpn1.1.running_var\n",
      "Pretrained dict:  model.segmentor.backbone.fpn1.1.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.backbone.fpn1.3.weight\n",
      "Pretrained dict:  model.segmentor.backbone.fpn1.3.bias\n",
      "Pretrained dict:  model.segmentor.backbone.fpn2.0.weight\n",
      "Pretrained dict:  model.segmentor.backbone.fpn2.0.bias\n",
      "Pretrained dict:  model.segmentor.backbone.norm.weight\n",
      "Pretrained dict:  model.segmentor.backbone.norm.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.conv_seg.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.conv_seg.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.0.1.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.0.1.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.0.1.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.0.1.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.0.1.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.0.1.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.1.1.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.1.1.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.1.1.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.1.1.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.1.1.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.1.1.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.2.1.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.2.1.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.2.1.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.2.1.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.2.1.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.2.1.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.3.1.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.3.1.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.3.1.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.3.1.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.3.1.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.psp_modules.3.1.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.bottleneck.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.bottleneck.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.bottleneck.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.bottleneck.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.bottleneck.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.bottleneck.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.0.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.0.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.0.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.0.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.0.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.0.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.1.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.1.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.1.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.1.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.1.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.1.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.2.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.2.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.2.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.2.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.2.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.lateral_convs.2.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.0.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.0.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.0.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.0.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.0.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.0.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.1.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.1.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.1.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.1.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.1.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.1.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.2.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.2.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.2.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.2.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.2.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_convs.2.bn.num_batches_tracked\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_bottleneck.conv.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_bottleneck.bn.weight\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_bottleneck.bn.bias\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_bottleneck.bn.running_mean\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_bottleneck.bn.running_var\n",
      "Pretrained dict:  model.segmentor.decode_head.fpn_bottleneck.bn.num_batches_tracked\n",
      "loading segmentor.backbone.cls_token\n",
      "loading segmentor.backbone.pos_embed\n",
      "loading segmentor.backbone.patch_embed.proj.weight\n",
      "loading segmentor.backbone.patch_embed.proj.bias\n",
      "loading segmentor.backbone.blocks.0.gamma_1\n",
      "loading segmentor.backbone.blocks.0.gamma_2\n",
      "loading segmentor.backbone.blocks.0.norm1.weight\n",
      "loading segmentor.backbone.blocks.0.norm1.bias\n",
      "loading segmentor.backbone.blocks.0.attn.q_bias\n",
      "loading segmentor.backbone.blocks.0.attn.v_bias\n",
      "loading segmentor.backbone.blocks.0.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.0.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.0.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.0.norm2.weight\n",
      "loading segmentor.backbone.blocks.0.norm2.bias\n",
      "loading segmentor.backbone.blocks.0.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.0.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.0.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.0.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.1.gamma_1\n",
      "loading segmentor.backbone.blocks.1.gamma_2\n",
      "loading segmentor.backbone.blocks.1.norm1.weight\n",
      "loading segmentor.backbone.blocks.1.norm1.bias\n",
      "loading segmentor.backbone.blocks.1.attn.q_bias\n",
      "loading segmentor.backbone.blocks.1.attn.v_bias\n",
      "loading segmentor.backbone.blocks.1.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.1.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.1.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.1.norm2.weight\n",
      "loading segmentor.backbone.blocks.1.norm2.bias\n",
      "loading segmentor.backbone.blocks.1.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.1.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.1.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.1.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.2.gamma_1\n",
      "loading segmentor.backbone.blocks.2.gamma_2\n",
      "loading segmentor.backbone.blocks.2.norm1.weight\n",
      "loading segmentor.backbone.blocks.2.norm1.bias\n",
      "loading segmentor.backbone.blocks.2.attn.q_bias\n",
      "loading segmentor.backbone.blocks.2.attn.v_bias\n",
      "loading segmentor.backbone.blocks.2.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.2.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.2.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.2.norm2.weight\n",
      "loading segmentor.backbone.blocks.2.norm2.bias\n",
      "loading segmentor.backbone.blocks.2.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.2.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.2.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.2.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.3.gamma_1\n",
      "loading segmentor.backbone.blocks.3.gamma_2\n",
      "loading segmentor.backbone.blocks.3.norm1.weight\n",
      "loading segmentor.backbone.blocks.3.norm1.bias\n",
      "loading segmentor.backbone.blocks.3.attn.q_bias\n",
      "loading segmentor.backbone.blocks.3.attn.v_bias\n",
      "loading segmentor.backbone.blocks.3.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.3.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.3.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.3.norm2.weight\n",
      "loading segmentor.backbone.blocks.3.norm2.bias\n",
      "loading segmentor.backbone.blocks.3.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.3.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.3.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.3.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.4.gamma_1\n",
      "loading segmentor.backbone.blocks.4.gamma_2\n",
      "loading segmentor.backbone.blocks.4.norm1.weight\n",
      "loading segmentor.backbone.blocks.4.norm1.bias\n",
      "loading segmentor.backbone.blocks.4.attn.q_bias\n",
      "loading segmentor.backbone.blocks.4.attn.v_bias\n",
      "loading segmentor.backbone.blocks.4.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.4.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.4.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.4.norm2.weight\n",
      "loading segmentor.backbone.blocks.4.norm2.bias\n",
      "loading segmentor.backbone.blocks.4.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.4.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.4.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.4.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.5.gamma_1\n",
      "loading segmentor.backbone.blocks.5.gamma_2\n",
      "loading segmentor.backbone.blocks.5.norm1.weight\n",
      "loading segmentor.backbone.blocks.5.norm1.bias\n",
      "loading segmentor.backbone.blocks.5.attn.q_bias\n",
      "loading segmentor.backbone.blocks.5.attn.v_bias\n",
      "loading segmentor.backbone.blocks.5.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.5.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.5.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.5.norm2.weight\n",
      "loading segmentor.backbone.blocks.5.norm2.bias\n",
      "loading segmentor.backbone.blocks.5.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.5.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.5.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.5.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.6.gamma_1\n",
      "loading segmentor.backbone.blocks.6.gamma_2\n",
      "loading segmentor.backbone.blocks.6.norm1.weight\n",
      "loading segmentor.backbone.blocks.6.norm1.bias\n",
      "loading segmentor.backbone.blocks.6.attn.q_bias\n",
      "loading segmentor.backbone.blocks.6.attn.v_bias\n",
      "loading segmentor.backbone.blocks.6.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.6.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.6.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.6.norm2.weight\n",
      "loading segmentor.backbone.blocks.6.norm2.bias\n",
      "loading segmentor.backbone.blocks.6.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.6.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.6.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.6.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.7.gamma_1\n",
      "loading segmentor.backbone.blocks.7.gamma_2\n",
      "loading segmentor.backbone.blocks.7.norm1.weight\n",
      "loading segmentor.backbone.blocks.7.norm1.bias\n",
      "loading segmentor.backbone.blocks.7.attn.q_bias\n",
      "loading segmentor.backbone.blocks.7.attn.v_bias\n",
      "loading segmentor.backbone.blocks.7.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.7.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.7.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.7.norm2.weight\n",
      "loading segmentor.backbone.blocks.7.norm2.bias\n",
      "loading segmentor.backbone.blocks.7.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.7.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.7.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.7.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.8.gamma_1\n",
      "loading segmentor.backbone.blocks.8.gamma_2\n",
      "loading segmentor.backbone.blocks.8.norm1.weight\n",
      "loading segmentor.backbone.blocks.8.norm1.bias\n",
      "loading segmentor.backbone.blocks.8.attn.q_bias\n",
      "loading segmentor.backbone.blocks.8.attn.v_bias\n",
      "loading segmentor.backbone.blocks.8.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.8.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.8.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.8.norm2.weight\n",
      "loading segmentor.backbone.blocks.8.norm2.bias\n",
      "loading segmentor.backbone.blocks.8.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.8.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.8.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.8.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.9.gamma_1\n",
      "loading segmentor.backbone.blocks.9.gamma_2\n",
      "loading segmentor.backbone.blocks.9.norm1.weight\n",
      "loading segmentor.backbone.blocks.9.norm1.bias\n",
      "loading segmentor.backbone.blocks.9.attn.q_bias\n",
      "loading segmentor.backbone.blocks.9.attn.v_bias\n",
      "loading segmentor.backbone.blocks.9.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.9.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.9.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.9.norm2.weight\n",
      "loading segmentor.backbone.blocks.9.norm2.bias\n",
      "loading segmentor.backbone.blocks.9.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.9.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.9.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.9.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.10.gamma_1\n",
      "loading segmentor.backbone.blocks.10.gamma_2\n",
      "loading segmentor.backbone.blocks.10.norm1.weight\n",
      "loading segmentor.backbone.blocks.10.norm1.bias\n",
      "loading segmentor.backbone.blocks.10.attn.q_bias\n",
      "loading segmentor.backbone.blocks.10.attn.v_bias\n",
      "loading segmentor.backbone.blocks.10.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.10.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.10.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.10.norm2.weight\n",
      "loading segmentor.backbone.blocks.10.norm2.bias\n",
      "loading segmentor.backbone.blocks.10.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.10.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.10.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.10.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.11.gamma_1\n",
      "loading segmentor.backbone.blocks.11.gamma_2\n",
      "loading segmentor.backbone.blocks.11.norm1.weight\n",
      "loading segmentor.backbone.blocks.11.norm1.bias\n",
      "loading segmentor.backbone.blocks.11.attn.q_bias\n",
      "loading segmentor.backbone.blocks.11.attn.v_bias\n",
      "loading segmentor.backbone.blocks.11.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.11.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.11.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.11.norm2.weight\n",
      "loading segmentor.backbone.blocks.11.norm2.bias\n",
      "loading segmentor.backbone.blocks.11.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.11.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.11.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.11.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.12.gamma_1\n",
      "loading segmentor.backbone.blocks.12.gamma_2\n",
      "loading segmentor.backbone.blocks.12.norm1.weight\n",
      "loading segmentor.backbone.blocks.12.norm1.bias\n",
      "loading segmentor.backbone.blocks.12.attn.q_bias\n",
      "loading segmentor.backbone.blocks.12.attn.v_bias\n",
      "loading segmentor.backbone.blocks.12.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.12.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.12.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.12.norm2.weight\n",
      "loading segmentor.backbone.blocks.12.norm2.bias\n",
      "loading segmentor.backbone.blocks.12.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.12.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.12.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.12.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.13.gamma_1\n",
      "loading segmentor.backbone.blocks.13.gamma_2\n",
      "loading segmentor.backbone.blocks.13.norm1.weight\n",
      "loading segmentor.backbone.blocks.13.norm1.bias\n",
      "loading segmentor.backbone.blocks.13.attn.q_bias\n",
      "loading segmentor.backbone.blocks.13.attn.v_bias\n",
      "loading segmentor.backbone.blocks.13.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.13.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.13.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.13.norm2.weight\n",
      "loading segmentor.backbone.blocks.13.norm2.bias\n",
      "loading segmentor.backbone.blocks.13.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.13.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.13.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.13.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.14.gamma_1\n",
      "loading segmentor.backbone.blocks.14.gamma_2\n",
      "loading segmentor.backbone.blocks.14.norm1.weight\n",
      "loading segmentor.backbone.blocks.14.norm1.bias\n",
      "loading segmentor.backbone.blocks.14.attn.q_bias\n",
      "loading segmentor.backbone.blocks.14.attn.v_bias\n",
      "loading segmentor.backbone.blocks.14.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.14.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.14.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.14.norm2.weight\n",
      "loading segmentor.backbone.blocks.14.norm2.bias\n",
      "loading segmentor.backbone.blocks.14.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.14.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.14.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.14.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.15.gamma_1\n",
      "loading segmentor.backbone.blocks.15.gamma_2\n",
      "loading segmentor.backbone.blocks.15.norm1.weight\n",
      "loading segmentor.backbone.blocks.15.norm1.bias\n",
      "loading segmentor.backbone.blocks.15.attn.q_bias\n",
      "loading segmentor.backbone.blocks.15.attn.v_bias\n",
      "loading segmentor.backbone.blocks.15.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.15.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.15.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.15.norm2.weight\n",
      "loading segmentor.backbone.blocks.15.norm2.bias\n",
      "loading segmentor.backbone.blocks.15.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.15.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.15.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.15.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.16.gamma_1\n",
      "loading segmentor.backbone.blocks.16.gamma_2\n",
      "loading segmentor.backbone.blocks.16.norm1.weight\n",
      "loading segmentor.backbone.blocks.16.norm1.bias\n",
      "loading segmentor.backbone.blocks.16.attn.q_bias\n",
      "loading segmentor.backbone.blocks.16.attn.v_bias\n",
      "loading segmentor.backbone.blocks.16.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.16.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.16.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.16.norm2.weight\n",
      "loading segmentor.backbone.blocks.16.norm2.bias\n",
      "loading segmentor.backbone.blocks.16.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.16.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.16.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.16.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.17.gamma_1\n",
      "loading segmentor.backbone.blocks.17.gamma_2\n",
      "loading segmentor.backbone.blocks.17.norm1.weight\n",
      "loading segmentor.backbone.blocks.17.norm1.bias\n",
      "loading segmentor.backbone.blocks.17.attn.q_bias\n",
      "loading segmentor.backbone.blocks.17.attn.v_bias\n",
      "loading segmentor.backbone.blocks.17.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.17.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.17.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.17.norm2.weight\n",
      "loading segmentor.backbone.blocks.17.norm2.bias\n",
      "loading segmentor.backbone.blocks.17.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.17.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.17.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.17.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.18.gamma_1\n",
      "loading segmentor.backbone.blocks.18.gamma_2\n",
      "loading segmentor.backbone.blocks.18.norm1.weight\n",
      "loading segmentor.backbone.blocks.18.norm1.bias\n",
      "loading segmentor.backbone.blocks.18.attn.q_bias\n",
      "loading segmentor.backbone.blocks.18.attn.v_bias\n",
      "loading segmentor.backbone.blocks.18.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.18.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.18.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.18.norm2.weight\n",
      "loading segmentor.backbone.blocks.18.norm2.bias\n",
      "loading segmentor.backbone.blocks.18.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.18.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.18.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.18.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.19.gamma_1\n",
      "loading segmentor.backbone.blocks.19.gamma_2\n",
      "loading segmentor.backbone.blocks.19.norm1.weight\n",
      "loading segmentor.backbone.blocks.19.norm1.bias\n",
      "loading segmentor.backbone.blocks.19.attn.q_bias\n",
      "loading segmentor.backbone.blocks.19.attn.v_bias\n",
      "loading segmentor.backbone.blocks.19.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.19.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.19.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.19.norm2.weight\n",
      "loading segmentor.backbone.blocks.19.norm2.bias\n",
      "loading segmentor.backbone.blocks.19.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.19.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.19.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.19.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.20.gamma_1\n",
      "loading segmentor.backbone.blocks.20.gamma_2\n",
      "loading segmentor.backbone.blocks.20.norm1.weight\n",
      "loading segmentor.backbone.blocks.20.norm1.bias\n",
      "loading segmentor.backbone.blocks.20.attn.q_bias\n",
      "loading segmentor.backbone.blocks.20.attn.v_bias\n",
      "loading segmentor.backbone.blocks.20.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.20.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.20.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.20.norm2.weight\n",
      "loading segmentor.backbone.blocks.20.norm2.bias\n",
      "loading segmentor.backbone.blocks.20.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.20.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.20.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.20.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.21.gamma_1\n",
      "loading segmentor.backbone.blocks.21.gamma_2\n",
      "loading segmentor.backbone.blocks.21.norm1.weight\n",
      "loading segmentor.backbone.blocks.21.norm1.bias\n",
      "loading segmentor.backbone.blocks.21.attn.q_bias\n",
      "loading segmentor.backbone.blocks.21.attn.v_bias\n",
      "loading segmentor.backbone.blocks.21.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.21.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.21.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.21.norm2.weight\n",
      "loading segmentor.backbone.blocks.21.norm2.bias\n",
      "loading segmentor.backbone.blocks.21.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.21.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.21.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.21.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.22.gamma_1\n",
      "loading segmentor.backbone.blocks.22.gamma_2\n",
      "loading segmentor.backbone.blocks.22.norm1.weight\n",
      "loading segmentor.backbone.blocks.22.norm1.bias\n",
      "loading segmentor.backbone.blocks.22.attn.q_bias\n",
      "loading segmentor.backbone.blocks.22.attn.v_bias\n",
      "loading segmentor.backbone.blocks.22.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.22.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.22.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.22.norm2.weight\n",
      "loading segmentor.backbone.blocks.22.norm2.bias\n",
      "loading segmentor.backbone.blocks.22.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.22.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.22.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.22.mlp.fc2.bias\n",
      "loading segmentor.backbone.blocks.23.gamma_1\n",
      "loading segmentor.backbone.blocks.23.gamma_2\n",
      "loading segmentor.backbone.blocks.23.norm1.weight\n",
      "loading segmentor.backbone.blocks.23.norm1.bias\n",
      "loading segmentor.backbone.blocks.23.attn.q_bias\n",
      "loading segmentor.backbone.blocks.23.attn.v_bias\n",
      "loading segmentor.backbone.blocks.23.attn.qkv.weight\n",
      "loading segmentor.backbone.blocks.23.attn.proj.weight\n",
      "loading segmentor.backbone.blocks.23.attn.proj.bias\n",
      "loading segmentor.backbone.blocks.23.norm2.weight\n",
      "loading segmentor.backbone.blocks.23.norm2.bias\n",
      "loading segmentor.backbone.blocks.23.mlp.fc1.weight\n",
      "loading segmentor.backbone.blocks.23.mlp.fc1.bias\n",
      "loading segmentor.backbone.blocks.23.mlp.fc2.weight\n",
      "loading segmentor.backbone.blocks.23.mlp.fc2.bias\n",
      "loading segmentor.backbone.fpn1.0.weight\n",
      "loading segmentor.backbone.fpn1.0.bias\n",
      "loading segmentor.backbone.fpn1.1.weight\n",
      "loading segmentor.backbone.fpn1.1.bias\n",
      "loading segmentor.backbone.fpn1.1.running_mean\n",
      "loading segmentor.backbone.fpn1.1.running_var\n",
      "loading segmentor.backbone.fpn1.1.num_batches_tracked\n",
      "loading segmentor.backbone.fpn1.3.weight\n",
      "loading segmentor.backbone.fpn1.3.bias\n",
      "loading segmentor.backbone.fpn2.0.weight\n",
      "loading segmentor.backbone.fpn2.0.bias\n",
      "loading segmentor.backbone.norm.weight\n",
      "loading segmentor.backbone.norm.bias\n",
      "loading segmentor.decode_head.conv_seg.weight\n",
      "loading segmentor.decode_head.conv_seg.bias\n",
      "loading segmentor.decode_head.psp_modules.0.1.conv.weight\n",
      "loading segmentor.decode_head.psp_modules.0.1.bn.weight\n",
      "loading segmentor.decode_head.psp_modules.0.1.bn.bias\n",
      "loading segmentor.decode_head.psp_modules.0.1.bn.running_mean\n",
      "loading segmentor.decode_head.psp_modules.0.1.bn.running_var\n",
      "loading segmentor.decode_head.psp_modules.0.1.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.psp_modules.1.1.conv.weight\n",
      "loading segmentor.decode_head.psp_modules.1.1.bn.weight\n",
      "loading segmentor.decode_head.psp_modules.1.1.bn.bias\n",
      "loading segmentor.decode_head.psp_modules.1.1.bn.running_mean\n",
      "loading segmentor.decode_head.psp_modules.1.1.bn.running_var\n",
      "loading segmentor.decode_head.psp_modules.1.1.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.psp_modules.2.1.conv.weight\n",
      "loading segmentor.decode_head.psp_modules.2.1.bn.weight\n",
      "loading segmentor.decode_head.psp_modules.2.1.bn.bias\n",
      "loading segmentor.decode_head.psp_modules.2.1.bn.running_mean\n",
      "loading segmentor.decode_head.psp_modules.2.1.bn.running_var\n",
      "loading segmentor.decode_head.psp_modules.2.1.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.psp_modules.3.1.conv.weight\n",
      "loading segmentor.decode_head.psp_modules.3.1.bn.weight\n",
      "loading segmentor.decode_head.psp_modules.3.1.bn.bias\n",
      "loading segmentor.decode_head.psp_modules.3.1.bn.running_mean\n",
      "loading segmentor.decode_head.psp_modules.3.1.bn.running_var\n",
      "loading segmentor.decode_head.psp_modules.3.1.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.bottleneck.conv.weight\n",
      "loading segmentor.decode_head.bottleneck.bn.weight\n",
      "loading segmentor.decode_head.bottleneck.bn.bias\n",
      "loading segmentor.decode_head.bottleneck.bn.running_mean\n",
      "loading segmentor.decode_head.bottleneck.bn.running_var\n",
      "loading segmentor.decode_head.bottleneck.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.lateral_convs.0.conv.weight\n",
      "loading segmentor.decode_head.lateral_convs.0.bn.weight\n",
      "loading segmentor.decode_head.lateral_convs.0.bn.bias\n",
      "loading segmentor.decode_head.lateral_convs.0.bn.running_mean\n",
      "loading segmentor.decode_head.lateral_convs.0.bn.running_var\n",
      "loading segmentor.decode_head.lateral_convs.0.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.lateral_convs.1.conv.weight\n",
      "loading segmentor.decode_head.lateral_convs.1.bn.weight\n",
      "loading segmentor.decode_head.lateral_convs.1.bn.bias\n",
      "loading segmentor.decode_head.lateral_convs.1.bn.running_mean\n",
      "loading segmentor.decode_head.lateral_convs.1.bn.running_var\n",
      "loading segmentor.decode_head.lateral_convs.1.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.lateral_convs.2.conv.weight\n",
      "loading segmentor.decode_head.lateral_convs.2.bn.weight\n",
      "loading segmentor.decode_head.lateral_convs.2.bn.bias\n",
      "loading segmentor.decode_head.lateral_convs.2.bn.running_mean\n",
      "loading segmentor.decode_head.lateral_convs.2.bn.running_var\n",
      "loading segmentor.decode_head.lateral_convs.2.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.fpn_convs.0.conv.weight\n",
      "loading segmentor.decode_head.fpn_convs.0.bn.weight\n",
      "loading segmentor.decode_head.fpn_convs.0.bn.bias\n",
      "loading segmentor.decode_head.fpn_convs.0.bn.running_mean\n",
      "loading segmentor.decode_head.fpn_convs.0.bn.running_var\n",
      "loading segmentor.decode_head.fpn_convs.0.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.fpn_convs.1.conv.weight\n",
      "loading segmentor.decode_head.fpn_convs.1.bn.weight\n",
      "loading segmentor.decode_head.fpn_convs.1.bn.bias\n",
      "loading segmentor.decode_head.fpn_convs.1.bn.running_mean\n",
      "loading segmentor.decode_head.fpn_convs.1.bn.running_var\n",
      "loading segmentor.decode_head.fpn_convs.1.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.fpn_convs.2.conv.weight\n",
      "loading segmentor.decode_head.fpn_convs.2.bn.weight\n",
      "loading segmentor.decode_head.fpn_convs.2.bn.bias\n",
      "loading segmentor.decode_head.fpn_convs.2.bn.running_mean\n",
      "loading segmentor.decode_head.fpn_convs.2.bn.running_var\n",
      "loading segmentor.decode_head.fpn_convs.2.bn.num_batches_tracked\n",
      "loading segmentor.decode_head.fpn_bottleneck.conv.weight\n",
      "loading segmentor.decode_head.fpn_bottleneck.bn.weight\n",
      "loading segmentor.decode_head.fpn_bottleneck.bn.bias\n",
      "loading segmentor.decode_head.fpn_bottleneck.bn.running_mean\n",
      "loading segmentor.decode_head.fpn_bottleneck.bn.running_var\n",
      "loading segmentor.decode_head.fpn_bottleneck.bn.num_batches_tracked\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load model\n",
    "model = CAEv2_seg()\n",
    "\n",
    "# load model weights\n",
    "# Download the weight from https://drive.google.com/drive/folders/1BsSjl1h3mxU6JNSbqvgZdyiTvV_2QBsH?usp=sharing\n",
    "model_path = '/data/wangzh/experiments/skinfm/finals/cae_seg_ham/lr_1e-4_decay_0.05_full/0/model_best_0.ckpt'\n",
    "pretrained_dict = torch.load(model_path, map_location=\"cpu\")\n",
    "pretrained_dict = pretrained_dict[\"state_dict\"]\n",
    "model_dict = model.state_dict()\n",
    "print('Model dict: ', model_dict.keys())\n",
    "available_pretrained_dict = {}\n",
    "\n",
    "for k, v in pretrained_dict.items():\n",
    "    print('Pretrained dict: ', k)\n",
    "    if k in model_dict.keys():\n",
    "        if pretrained_dict[k].shape == model_dict[k].shape:\n",
    "            available_pretrained_dict[k] = v\n",
    "    if k[6:] in model_dict.keys():\n",
    "        if pretrained_dict[k].shape == model_dict[k[6:]].shape:\n",
    "            available_pretrained_dict[k[6:]] = v\n",
    "\n",
    "for k, _ in available_pretrained_dict.items():\n",
    "    print(\"loading {}\".format(k))\n",
    "model_dict.update(available_pretrained_dict)\n",
    "model.load_state_dict(model_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inference\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    output = model(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save result\n",
    "image_save = image.squeeze().cpu().detach().numpy()\n",
    "image_save = image_save * 0.5 + 0.5\n",
    "image_save = np.transpose(image_save, (1, 2, 0))\n",
    "\n",
    "output = torch.argmax(output.squeeze(), dim=0).cpu().detach().numpy()\n",
    "output = largestConnectComponent(output)\n",
    "\n",
    "output = mark_boundaries(image_save, output, color=(0, 1, 1), mode='thick')\n",
    "output = (output * 255).astype(np.uint8)\n",
    "\n",
    "cv2.imwrite(os.path.join(save_path, 'result.png'), output[..., ::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(output)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "skinfm",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
